A massively parallel reverse modeling approach for semiconductor devices and circuits

Single-hidden-layer feed-forward quantum neural network based on Grover learning

Artificial intelligent provides diverse solutions for the complex problems in agriculture research. The study aimed to use three models of artificial neural networks (Feed Forward Neural Network (FFNN), Generalized Regression Neural Network (GRNN) and Radial-Basis Neural Network (RBNN)) in the field of wheat yield prediction. 27-year data for the period (1986-2012) were utilized to improve the models and four-year data (2013 and 2016) were used to estimate the models, to compare their outputs with the measured data. Prediction data was not entered in the process of building neural network models. The results showed that the optimal configuration of the FFNN model consists of 40 neurons in the hidden layer (8-40-1). The Tan Sigmoid activation function was used in both the hidden layer and the output layer using all of these models (anterior neural feeding network and the regression neural network and radial base neural network) in the 4-year wheat yield forecast field for production (2013-2016) by applying 8 input parameters that were result of NMMS (8.6%, 7.6% and 15.7% resp.), To find that FFNN and GRNN provide the best result from BRNN because while the information set was large or in a wide range, then the range data ranges from -1 to +1 (normalization data) , GRNN gives better outcomes after the information or sample data were in large range.

In present days, real time navigation depends on Kalman filter to fuse data from Global Positioning System (GPS) and Inertial Navigation System (INS). But there exist drawbacks like long design time, requirement of prior knowledge in fusing data from GPS and INS using Kalman Filter. In order to overcome the drawbacks of Kalman filter, GPS and INS integration are done using Artificial Neural Networks (ANN) like FFNNs. But still there exists certain drawbacks like less accuracy in FFNNs for GPS and INS data integration. Therefore, this paper introduces Higher Order Neural Networks (HONNs) approach for INS and GPS data integration to overcome the drawback. A higher order feed forward neural network architecture with optimum number of nodes is used. In the given architectures, the replacement of summation at each node by multiplication results in more powerful mapping, because of its capability of processing higher-order information from training data. Performance comparison of HONNs with FFNNs shows that the proposed architecture provides satisfactory results in terms of error rate and number of epochs. The HONNs like Multiplicative Neural Network (MNN) module and Sigma-Pi-ANN module and Traditional Feed Forward Neural Networks(FFNNs) like Radial Basis Function Neural Network (RBF) module and Back propagation Neural Network (BPN) module are trained to predict the INS position error and provide accurate position of the moving aircrafts. Sigma-Pi ANN is found to be the best in terms of accuracy, number of epochs and execution time among the two HONNs.

This chapter provides some examples from the literature of how feed-forward neural networks are used in three different contexts in manufacturing operations. Operational design problems involve the determination of design parameters, such as number of kanbans, in order to optimize the performance of the system. Operational-system decision support refers to the use of neural networks as decision-support mechanisms in predicting system performance in response to certain settings of system parameters and current environmental factors. Operational-system-control problems are distinguished from decision support in that the consequences of a control decision are both an immediate return and putting the system in a new state from which another control decision needs to be taken. In operational control, new ideas are emerging using neural networks in approximate dynamic programming. Manufacturing systems can be very complex. There are many factors that may influence the performance of these systems; yet in many cases, the true relationship between these factors and the system outcomes is not fully understood. Neural networks have been given a great deal of attention in recent years with their ability to learn complex mappings even when presented with a partial, and even noisy, set of data. This has resulted in their being considered as a means to study and perhaps even optimize the performance of manufacturing operations. This chapter provides some examples from the literature of how neural networks are used in three different contexts in manufacturing systems. The categories (1) operational design, (2) operational decision-support systems, and (3) operational control are distinguished by the time context within which the models are used. Some examples make use of simulation models to produce training data, while some use actual production data. In some applications, the network is used to simply predict performance or outcomes, while in others the neural network is used in the determination of optimal parameters or to recommend good settings. Readers who wish to explore further examples of neural networks in manufacturing can examine Udo (1992), Zhang and Huang (1995), and Wang, Tang, and Roze (2001). We begin with two areas in which neural networks have found extensive use in manufacturing. Operational-system design has seen considerable use of neural networks as metamodels that can stand in place of the system, as we attempt to understand its behavior and optimize design parameters. Operational-system decision support refers to the use of neural networks as decision-support mechanisms in predicting system performance in response to certain settings of system parameters. We close with a short introduction to an area where we anticipate seeing growing numbers of applications, namely the use of approximate dynamic programming methods to develop real-time controllers for manufacturing systems.

Background Urinary tract infections (UTIs) are a common cause of healthcare visits and hospitalizations. A recent study attributed about 8.7 million ER visits to UTIs from 2016 to 20231. Asymptomatic bacteriuria (ASB) is often mistaken for a UTI, leading to unnecessary treatment and the development of resistant bacteria. This study aims to develop a deep learning model to differentiate ASB from a UTI based on urinalysis parameters and to compare its performance to traditional statistical methods, specifically logistic regression.Figure 1.Feedforward neural network model with 3 hidden layers and 2, 128, and 8 nodes in their respective layers.Figure 2.Proportion of ASB (Blue) vs. a UTI (Red) of urinalysis parameters. Methods This retrospective case-control study included adult patients with ASB or UTIs from January 1st, 2010 to December 31st, 2022. Urinalysis parameters and urine culture results were collected. A feedforward neural network with 30 input features and 2 hidden layers was trained and tuned to predict either ASB or a UTI (Figure 1). The model's performance was compared to a stepwise bidirectional logistic regression model. Results 272 patient encounters were included (85 UTI, 187 ASB). The neural network model was found to be the optimal hyperparameter for the model based on the highest validation AUC of 0.69 (Figure 1). The tuned model achieved a binary accuracy of 0.83, precision of 0, and recall of 0 on the validation set with batch size of 4 and learning rate of 0.0003. The testing set showed an AUC of 0.35, binary accuracy of 0.842, precision of 0, and recall of 0 (Figure 2). The stepwise bidirectional logistic regression model, with an AIC of 270.65, demonstrated an accuracy of 0.745, but a precision of 0, a recall of 0, and an AUC of 0.525. Statistically significant parameters (p< 0.01) included red color (estimate: 0.6956), straw color (estimate: -0.292), and densely turbid clarity (estimate: 1.696). Conclusion While the neural network model showed some predictive capabilities, the logistic regression model had higher accuracy. However, it also lacked precision and recall, indicating limitations in its ability to correctly identify UTI cases. Future tests with larger datasets may improve the models' accuracy in differentiating ASB from a UTI, if such patterns exist. Our results highlight the complexity of developing a robust predictive model for differentiating ASB vs. a UTI and the need for continued research on the topic. Disclosures All Authors: No reported disclosures

A multilayer feedforward neural network with a modification to the standard backpropagation training of neural nets is considered. Information on the gradients of target outputs with respect to network inputs is used in this modification. This modified neural network is then applied for approximating design requirements of aerospace composite components, such as an aircraft engine guide vane and a satellite reflector assembly. This neural network functional approximator is then used with an optimization algorithm for determining the optimal parameters for component design requirements. The modified neural network provides for faster convergence of learning process and requires a smaller number of hidden layer nodes for convergence to similar training error levels. HE application of neural networks as universal approximators has been the focus of tremendous amount of activity in the past few years.16 Neural networks have been used for approximating and modeling both memoryless systems and dynamical systems with memory. This paper does not go into the various neural net architectures and training schemes, and the interested reader could refer to standard texts on this subject matter.1'2 This paper considers static neural networks (systems without memory) formed by interconnections without loops, called feedforward networks.2 In addition, this work considers a backpropagation learning scheme with adaptation of learning rate. There have been many additions to, or adaptations of, the basic backpropagation scheme, such as adaptive learning rate, learning with momentum, and the like,1'2 for improving convergence of the learning scheme, and these are not addressed in this paper. This paper explores a modification of the backpropagation learning scheme using information on the gradient of the target outputs with respect to the inputs of the network. The performance of the modified learning scheme is compared with that of the standard backpropagation learning scheme.2 A feedforward network with a single hidden layer trained with this modified learning scheme is applied in structural optimization problems. Figure 1 shows a feedforward net with a single hidden layer. The restriction to a single hidden layer is not a limitation of the modified learning scheme, and the new training scheme can be used on networks with multiple hidden layers also. The use of neural networks for structural response approximation has been reported by several authors.46 The primary reason for investigating such properties is to assess the applicability of trained neural networks for quick and accurate approximation of response functions during structural design optimization. Structural optimization, utilizing either gradient-based numerical search or genetic search algorithms, requires repeated function (response) evaluations during the search process. A single finite element analysis of large-scale structures requires a significant amount of computational time and resource and hence the need for robust approxima

Internal solitary waves exist widely in the oceans, and their generations, propagation evolutions, and dissipations have profound effects on the ocean environment, topography, and marine structures. Typically, two basic theoretical models are now being developed to govern the evolutions of internal solitary waves at the interface of two immiscible inviscid fluids. One is a unidirectional wave propagation model described by the KdV (Korteweg-de Vries) equation, and the other is a bidirectional wave propagation model depicted by the Miyata-Choi-Camassa (MCC) equation. Neither of them, however, can effectively characterize the course of the evolution of large-amplitude internal solitary wave. In this paper, a modified unidirectional internal solitary wave model is established by adjusting the coefficients of the original unidirectional model. The adjusted coefficients are determined through asymptotic analysis by matching with the MCC model. The efficacy of the modified coefficients is investigated by comparing the modified model with the original model. The experiments on the generation of internal solitary waves with varying amplitudes are carried out by comparing the internal solitary wave solution of the modified equation. It is shown that the modified model is suitable for describing the waveform of internal solitary waves with small, medium, and large amplitudes within the limiting amplitude of the MCC model. By quantitatively analyzing the agreement of the effective wavelength, wave speed, and waveform of steady-state internal solitary waves between the unidirectional model and the MCC model, the applicability of the modified model in characterization of the properties of steady-state internal solitary waves is further investigated. In addition, the stability of unidirectional theoretical model is analyzed for simulating the propagation of large-amplitude internal solitary wave under flat bottom condition. It is found that the unidirectional model is suitable for initiating its own internal solitary solution provided that the numerical scheme is stable. It is shown that the modified unidirectional model can be used to characterize large-amplitude internal solitary waves, and is also expected to be applied to the study of marine structure hydrodynamics.

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.

The adoption of wind energy has grown significantly in recent years. New, cost-effective technologies have been developed, led by customer awareness of green technologies and a legal framework proposed at the European Union level. The stochastic nature of wind speed is transferred to wind turbine output, making wind energy difficult to predict. The main scope of predicting wind energy production is to be proactive in balancing and reserving energy to meet demand. When the prediction identifies a potential gap between supply and demand, additional energy from other sources must be generated and supplied. Creating a synergy of physical devices through advanced sensing capabilities, software, storage and analytics capabilities, the Industrial Internet of Things is enabling the effective transition to wind energy through automation by removing many of the disadvantages in a way that has recently become accessible. This research focuses on the data analytics, proposing a fast univariate network-based approach for wind energy prediction, using Feed Forward Neural Networks, Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Unit, and Convolutional Neural Networks. Moreover, by introducing the theoretical fundamentals, the implementation method and the hyperparameters of the final models, this article becomes unique in the context of wind energy. At the time of this study, no prior research studies have presented a direct comparison between feedforward, recurrent, and convolutional neural networks ? these being the most important in the field of supervised learning.

On the Circuit Complexity of Sigmoid Feedforward Neural Networks

The application of artificial neural networks to the estimation of bioprocess variables will be discussed. In fermentation processes, direct on-line measurements of primary process variables usually are unavailable. The state of the cultivation, therefore, has to be inferred from measurements of secondary variables and any previous knowledge of process dynamics. This research investigates the learning, recall and generalization characteristics of neural networks trained to model the nonlinear behavior of a fermentation process. Two different neural network methodologies are discussed, namely, feed-forward and recurrent neural networks, which differ in their treatment of time dependence. The neural networks are trained by backpropagation using a conjugate gradient technique, which provides a dramatic improvement in the convergence speed. The objective is to use environmental and physiological information available from on-line sensors to estimate concentrations of species in a bioreactor. Results of the neural network estimators are presented, based on experimental data available from the ethanol production by Zymomonas mobilis fermentation. The feed-forward and recurrent neural network methodologies are demonstrated to perform suitably as unmeasurable state estimators. Both networks offer comparable abilities of recall, but recurrent networks perform better than feed-forward networks in generalization.

On-line Estimation of Bioreactors using Recurrent Neural Networks

The Sixth International Symposium on Neural Networks (ISNN 2009)

This paper aims to represent a proposition of an innovative and novel methodology applicable in detecting and classifying the power quality disturbances present in the supply to the induction motor. In all practicality, considering circumstantial real world applications, induction motors are usually operated on load. If the supply voltage is varied in any way, it would adversely affect the normal operation of the motor. In the present work, a healthy induction motor is subjected to power quality disturbances like balanced voltage sag, balanced voltage swell, unbalanced voltage sag and unbalanced voltage swell. For the purpose of detecting these power quality disturbances, discrete wavelet transform is applied to the stator current of the induction motor. The stator current wavelet coefficients are fed as input to the neural network for the classification purpose. Radial basis neural network and feed forward neural network have been independently trained and tested. The observation about the feedforward network having higher performance efficiency as compared to the radial basis network, has been seen.

In this paper, we showed a method to forecast the stock price using neural networks. Predicting the stock market is very difficult since it depends on several known and unknown factors. In recent years, one of the techniques that have been used popularly in this area is artificial neural network. The power of neural network is its ability to model a nonlinear process without a priori knowledge about the nature of the process. We used both feed forward neural network and simple recurrent neural network, trained by time and profit based back propagation algorithm with early stopping to make the prediction. The integration of profit and time factors with training procedure made an improvement in forecasted results for feed forward neural network. Moreover, the simple recurrent neural network with its ` time capture' capabilities had better forecasted results than feed forward neural network in all experiments.